esci in jamovi

esci is now available as a module in jamovi!

esci (exploratory software for confidence intervals)

jamovi (the free, open-source stats software that crushes SPSS)

a match made in heaven.

Get esci for jamovi:

  • Download and install the current version of jamovi (be sure to select the current version not the solid version), https://www.jamovi.org/download.html
  • Open jamovi, and then under the modules menu (a big + sign), select “jamovi library”

  • The window that appears shows the various modules available for jamovi.  Browse to find esci and then click “Install”

  • If all goes well you’ll end up with an esci menu in jamovi.

What is esci for jamovi?

Geoff has long been working on to make it easy to adopt and use the new statistics.  Back in 2001 he released the first version of ESCI (Exploratory Software for Confidence Intervals), a set of Excel worksheets that provided:

  • Simulations to explore key statistical ideas (e.g. the dance of the means)
  • Analysis pages to provide effect sizes and confidence intervals for most basic study designs (including meta-analysis)
  • Beautiful graphs focused on visualizing uncertainty

Over the years, ESCI has been updated and expanded, with major new releases accompanying the publication of Understanding the New Statistics and the first edition of Introduction to the New Statistics.

esci for jamovi just one part of a big step forward for these efforts.  esci is no longer tied to Excel, but is now written in R in a way that allows it to be plugged in as a module for jamovi .  If that doesn’t mean anything to you, try this:  You can now adopt a new-statistics approach using modern (and free) statistical software.  Estimation just got even easier for all.  Huzzah.

The second edition of Introduction to the New Statistics has now been updated to use this new version of esci.  It offers everything that used to be accomplished in Excel and more, but now with easy data file handling and natural progression paths to the whole world of statistical software, beyond the intro course.

What can you do with this module?

esci in jamovi now supports most of the basic analyses you would learn in an undergraduate statistics course and meta-analysis (which really should be part of a good undergraduate statistics course). 

Here’s how you would map traditional hypothesis tests onto the analyses available in esci:

Traditional hypothesis test esci in jamovi command
One-sample t-test Means and Medians: Single Group
Independent samples t-test Means and Medians: Two Groups
Paired samples t-test Means and Medians: Paired
One-Way ANOVA Means and Medians: Independent Groups Contrasts
2×2 ANOVA Means and Medians: 2×2 Factorial
2×2 Chi Squared Proportions: Two Groups
Correlation test Correlations: Single Group
Correlation test with Categorical Moderator Correlations: Two Groups

In addition, you’ll find analyses to conduct meta-analysis for means, 2-group designs (from raw data or Cohen’s d) , for correlations, proportions, and two-group proportions, all with the option to analyze categorical moderators. 

Your feedback is needed:

We’d be very excited to have your feedback, feature requests, and/or bug reports.  Please especially consider esci through the eyes of your students:

  • What other analyses would you like to see?
  • Anything in the output that is hard to understand?  That should be labelled better?  That should be added or could be removed?
  • Would it be helpful to add the option to see all assumptions for an analysis?  Should we provide more guidance on interpreting output? 
  • Any options missing from analyses?

The best way to provide feedback would be on the github page for this module, which is here: https://github.com/rcalinjageman/esci.  If that’s a hassle, then by all means just email Bob directly or tweet at us @TheNewStats

As you provide feedback, keep in mind a couple of key design goals for this module:

  • For now, esci will only support frequentist statistics.  A long-term goal would be to make it easy to also obtain Bayesian estimates and bootstrapped estimates, but that’s not in the cards for now.  In the meantime, if you want bootstrapped analyses, DaBest already makes this easy: http://www.estimationstats.com/#/
  • We want esci to be useful for researchers, but also accessible for students.  So we’ve tried to make this easy to use, have tried to provide lots of feedback and error-checking to prevent mistakes (no more means of gender, please!), and have tried to keep output very straightforward.  We’re still feeling our way here.
  • We’ve tried to keep esci ‘lightweight’, with relatively few dependencies on other R packages. 
  • We’ve separated out the exploration/simulation aspects of esci (like the dance of the means) to focus just on analysis.  These are now fully online here:

Thank you

There’s still a lot of work to go on this module, but it is probably never too early to be grateful for those who have provided help and assistance.  These would include:

  • Students in Bob’s 2019-2020 statistics and neuroscience classes, who helped put this module through its paces.  Thanks, and may you never have to sideload again.
  • Adam Claridge-Chang and Joses Ho, developers of DABEST.  We benefited tremendously from adopting portions of their source code for esci. 
  • Jonathon Love, Damian Dropmann, and Ravi Selker– the jamovi team.  Jonathon was especially helpful (and patient) with suggesting improvements to the module.
  • Matthew Kay, developer of ddist, which makes the lovely uncertainty visualizations in esci.  Matthew answered lots of dumb questions that helped esci along.

Also in R, you said?

Yes, under the hood of this jamovi module is an R package for estimation.  You can grab it from github and we’ll soon be submitting it to CRAN.

If you’re a JASP user, stay tuned–we’re also exploring possibilities for a module there as well.  The dream is one code base for a well-documented and tested R package that can easily be plugged into a GUI environment.  That will be estimation for all. 

An example:

ManyLabs1 included a replication of Nosek et al. (2002) in which students who identified as male and female were asked to take an implicit association test (IAT) measuring attitudes towards math vs. art.  The key research question is to what extent males and females differ in their implicit attitudes towards math. 

Here’s data for the valid participants from the OSU lab from ManyLabs 1 (we obtained this data from the OSF site for ManyLabs 1) in .csv format.  You can open this directly in ESCI.

The effect size of interest is the mean difference between two groups, so we will use “Means and Medians: Two Groups” from the ESCI menu. 

We then specify d_art as the outcome variable (this is the variable showing the IAT score, where scores over 0 represent more positive attitudes towards art, scores less than 0 represent more positive attitudes towards math, and a score of 0 indicates similar attitudes).  For the grouping variable, we enter sex (the gender that participant identifies with).

For output we get a table of means that includes the key effect size: the estimated mean difference with a confidence interval.

We also get a standardized effect size with confidence interval.

And we get this lovely visualization, one which shows all the data, which graphically shows the effect size, and which works to make uncertainty of the estimate salient (hat tip to the fantastic ggdist package by Matthew Kay)

Other details:

esci is free and open source.

Source code is on github: https://github.com/rcalinjageman/esci

Current road map:

  • Refactor underlying R code for a consistent functions and results objects.  Follow R style throughout and add tests and documentation for all functions